Potential customer mining application of smart home products based on LightGBM PU learning and Spark ML algorithm practice

This paper studies the case of big data-based intelligent product potential customer mining internal competition in China Telecom Shanghai Company. Huge amounts of data based on big data table, the use of machine Learning and data analysis technology, using the algorithm of LightGBM, PySpark machine Learning algorithms, Positive Unlabeled Learning algorithm, and predict whether customers buy whole house product, precision marketing into artificial intelligence for the customer, large data capacity, promote the development of intelligent products of the company.